Trustworthy Intrusion Detection: Confidence Estimation Using Latent Space

• URL: http://arxiv.org/abs/2409.13774v1
• Date: Thu, 19 Sep 2024 08:09:44 GMT
• Title: Trustworthy Intrusion Detection: Confidence Estimation Using Latent Space
• Authors: Ioannis Pitsiorlas, George Arvanitakis, Marios Kountouris,
• Abstract summary: This work introduces a novel method for enhancing confidence in anomaly detection in Intrusion Detection Systems (IDS) By developing a confidence metric derived from latent space representations, we aim to improve the reliability of IDS predictions against cyberattacks. Applying to the NSL-KDD dataset, our approach focuses on binary classification tasks to effectively distinguish between normal and malicious network activities.
• Score: 7.115540429006041
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This work introduces a novel method for enhancing confidence in anomaly detection in Intrusion Detection Systems (IDS) through the use of a Variational Autoencoder (VAE) architecture. By developing a confidence metric derived from latent space representations, we aim to improve the reliability of IDS predictions against cyberattacks. Applied to the NSL-KDD dataset, our approach focuses on binary classification tasks to effectively distinguish between normal and malicious network activities. The methodology demonstrates a significant enhancement in anomaly detection, evidenced by a notable correlation of 0.45 between the reconstruction error and the proposed metric. Our findings highlight the potential of employing VAEs for more accurate and trustworthy anomaly detection in network security.

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